A method for characterizing daily physiology from widely used wearables
Summary: Millions of wearable-device users record their heart rate (HR) and activity. We introduce a statistical method to extract and track six key physiological parameters from these data, including an underlying circadian rhythm in HR (CRHR), the direct effects of activity, and the effects of mea...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-08-01
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| Series: | Cell Reports: Methods |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667237521001065 |
| _version_ | 1819114191042117632 |
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| author | Clark Bowman Yitong Huang Olivia J. Walch Yu Fang Elena Frank Jonathan Tyler Caleb Mayer Christopher Stockbridge Cathy Goldstein Srijan Sen Daniel B. Forger |
| author_facet | Clark Bowman Yitong Huang Olivia J. Walch Yu Fang Elena Frank Jonathan Tyler Caleb Mayer Christopher Stockbridge Cathy Goldstein Srijan Sen Daniel B. Forger |
| author_sort | Clark Bowman |
| collection | DOAJ |
| description | Summary: Millions of wearable-device users record their heart rate (HR) and activity. We introduce a statistical method to extract and track six key physiological parameters from these data, including an underlying circadian rhythm in HR (CRHR), the direct effects of activity, and the effects of meals, posture, and stress through hormones like cortisol. We test our method on over 130,000 days of real-world data from medical interns on rotating shifts, showing that CRHR dynamics are distinct from those of sleep-wake or physical activity patterns and vary greatly among individuals. Our method also estimates a personalized phase-response curve of CRHR to activity for each individual, representing a passive and personalized determination of how human circadian timekeeping continually changes due to real-world stimuli. We implement our method in the “Social Rhythms” iPhone and Android app, which anonymously collects data from wearable-device users and provides analysis based on our method. Motivation: The exploding popularity of wearable devices, now a multi-billion dollar industry, provides a new opportunity for real-world data collection. Here, we propose a statistical method for analysis of ambulatory wearable-device data that can estimate circadian rhythms. Accounting for circadian rhythms in HR will allow more accurate measurement of other physiological parameters, e.g., basal HR, how activity increases HR, and changes in HR due to infection. |
| first_indexed | 2024-12-22T04:41:22Z |
| format | Article |
| id | doaj.art-58e021495b4440aaa4a5088ae4739a24 |
| institution | Directory Open Access Journal |
| issn | 2667-2375 |
| language | English |
| last_indexed | 2024-12-22T04:41:22Z |
| publishDate | 2021-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Cell Reports: Methods |
| spelling | doaj.art-58e021495b4440aaa4a5088ae4739a242022-12-21T18:38:44ZengElsevierCell Reports: Methods2667-23752021-08-0114100058A method for characterizing daily physiology from widely used wearablesClark Bowman0Yitong Huang1Olivia J. Walch2Yu Fang3Elena Frank4Jonathan Tyler5Caleb Mayer6Christopher Stockbridge7Cathy Goldstein8Srijan Sen9Daniel B. Forger10Department of Mathematics and Statistics, Hamilton College, Clinton, NY, USADepartment of Mathematics, Dartmouth College, Hanover, NH, USADepartment of Neurology, University of Michigan, Ann Arbor, MI, USAMolecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI, USAMolecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI, USADepartment of Mathematics, University of Michigan, Ann Arbor, MI, USADepartment of Mathematics, University of Michigan, Ann Arbor, MI, USALSA Technology Services, University of Michigan, Ann Arbor, MI, USADepartment of Neurology, University of Michigan, Ann Arbor, MI, USAMolecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI, USADepartment of Mathematics, University of Michigan, Ann Arbor, MI, USA; Department of Computational Medicine and Bioinformatics, Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI, USA; Corresponding authorSummary: Millions of wearable-device users record their heart rate (HR) and activity. We introduce a statistical method to extract and track six key physiological parameters from these data, including an underlying circadian rhythm in HR (CRHR), the direct effects of activity, and the effects of meals, posture, and stress through hormones like cortisol. We test our method on over 130,000 days of real-world data from medical interns on rotating shifts, showing that CRHR dynamics are distinct from those of sleep-wake or physical activity patterns and vary greatly among individuals. Our method also estimates a personalized phase-response curve of CRHR to activity for each individual, representing a passive and personalized determination of how human circadian timekeeping continually changes due to real-world stimuli. We implement our method in the “Social Rhythms” iPhone and Android app, which anonymously collects data from wearable-device users and provides analysis based on our method. Motivation: The exploding popularity of wearable devices, now a multi-billion dollar industry, provides a new opportunity for real-world data collection. Here, we propose a statistical method for analysis of ambulatory wearable-device data that can estimate circadian rhythms. Accounting for circadian rhythms in HR will allow more accurate measurement of other physiological parameters, e.g., basal HR, how activity increases HR, and changes in HR due to infection.http://www.sciencedirect.com/science/article/pii/S2667237521001065wearablesHR analysisappscircadian rhythmsphase-response curves |
| spellingShingle | Clark Bowman Yitong Huang Olivia J. Walch Yu Fang Elena Frank Jonathan Tyler Caleb Mayer Christopher Stockbridge Cathy Goldstein Srijan Sen Daniel B. Forger A method for characterizing daily physiology from widely used wearables Cell Reports: Methods wearables HR analysis apps circadian rhythms phase-response curves |
| title | A method for characterizing daily physiology from widely used wearables |
| title_full | A method for characterizing daily physiology from widely used wearables |
| title_fullStr | A method for characterizing daily physiology from widely used wearables |
| title_full_unstemmed | A method for characterizing daily physiology from widely used wearables |
| title_short | A method for characterizing daily physiology from widely used wearables |
| title_sort | method for characterizing daily physiology from widely used wearables |
| topic | wearables HR analysis apps circadian rhythms phase-response curves |
| url | http://www.sciencedirect.com/science/article/pii/S2667237521001065 |
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